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Record W2787836236 · doi:10.1109/lsp.2018.2802780

Is Self-Interference in Full-Duplex Communications a Foe or a Friend?

2018· article· en· W2787836236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Signal Processing Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsTelecommunications linkBase stationMaximizationTransmission (telecommunications)Iterative methodConvex optimization

Abstract

fetched live from OpenAlex

This letter studies the potential of harvesting energy from the self-interference of a full-duplex base station. The base station is equipped with a self-interference cancellation switch, which is turned off for a fraction of the transmission period in order to harvest the energy from the self-interference that arises due to the downlink transmission. For the remaining transmission period, the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission. A novel energy-efficiency maximization problem is formulated for the joint design of downlink beamformers, uplink power allocations, and the transmission time-splitting factor. The optimization problem is nonconvex, and hence, a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach. Numerical simulation results show significant improvement in the energy-efficiency by allowing self-energy recycling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.279
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it